module TensorStream
  # Class that provides auto-differentiation
  class MathGradients
    extend TensorStream::OpHelper

    def self.tf
      TensorStream
    end

    def self.derivative(tensor, wrt_dx, options = {})
      return i_op(:ones_like, tensor) if tensor.equal?(wrt_dx)
      return i_op(:zeros_like, tensor) unless wrt_dx.consumers.include?(tensor.name)

      nodes_to_compute = wrt_dx.consumers.select do |t|
        node = tensor.graph.nodes[t]
        node.consumers.include?(tensor.name) || node.equal?(tensor)
      end.compact + [wrt_dx.name]

      grad = i_op(:fill, tf.shape(tensor), tf.constant(1, dtype: wrt_dx.data_type))

      _propagate(grad, tensor, wrt_dx, nodes_to_compute, options[:stop_gradients] || []) || i_op(:zeros_like, wrt_dx)
    end

    def self._propagate(grad, tensor, stop_tensor, nodes_to_compute, stop_gradients = [])
      return grad if stop_tensor.equal?(tensor)
      return nil if stop_gradients && _include?(stop_gradients, tensor)
      return nil unless tensor.is_a?(Operation)

      computed_op = _compute_derivative(tensor, grad)

      if computed_op.is_a?(Array)
        computed_op.each_with_index.collect do |op_grad, index|
          next if op_grad.nil?
          next unless nodes_to_compute.include?(tensor.inputs[index].name)

          _propagate(op_grad, tensor.inputs[index], stop_tensor, nodes_to_compute, stop_gradients)
        end.compact.reduce(:+)
      else
        return nil if computed_op.nil?
        _propagate(computed_op, tensor.inputs[0], stop_tensor, nodes_to_compute, stop_gradients)
      end
    end

    def self._compute_derivative(node, grad)
      node.graph.name_scope("#{node.name}_grad") do
        x = node.inputs[0] if node.inputs[0]
        y = node.inputs[1] if node.inputs[1]

        case node.operation
        when :add_n
          return [grad] * node.inputs.size
        when :add
          return [grad, grad] if shapes_fully_specified_and_equal(x, y)
          sx = tf.shape(x, name: 'add/shape_x')
          sy = tf.shape(y, name: 'add/shape_y')
          rx, ry = _broadcast_gradient_args(sx, sy)

          [tf.reshape(tf.reduce_sum(grad, rx, name: 'add/reduce_sum_x'), sx),
           tf.reshape(tf.reduce_sum(grad, ry, name: 'add/reduce_sum_y'), sy)]
        when :asin
          tf.control_dependencies([grad]) do
            x2 = tf.square(x)
            one = tf.constant(1, dtype: grad.data_type)
            den = tf.sqrt(tf.subtract(one, x2))
            inv = tf.reciprocal(den)
            grad * inv
          end
        when :acos
          tf.control_dependencies([grad]) do
            x2 = tf.square(x)
            one = tf.constant(1, dtype: grad.data_type)
            den = tf.sqrt(tf.subtract(one, x2))
            inv = tf.reciprocal(den)
            -grad * inv
          end
        when :sub
          return [grad, -grad] if shapes_fully_specified_and_equal(x, y)

          sx = tf.shape(x, name: 'sub/shape_x')
          sy = tf.shape(y, name: 'sub/shape_y')
          rx, ry = _broadcast_gradient_args(sx, sy)

          [tf.reshape(tf.reduce_sum(grad, rx, name: 'add/reduce_sub_x'), sx),
           -tf.reshape(tf.reduce_sum(grad, ry, name: 'add/reduce_sub_y'), sy)]
        when :mul
          sx = tf.shape(x)
          sy = tf.shape(y)
          rx, ry = _broadcast_gradient_args(sx, sy)

          [tf.reshape(tf.reduce_sum(tf.mul(grad, y), rx), sx),
           tf.reshape(tf.reduce_sum(tf.mul(x, grad), ry), sy)]
        when :div
          sx = i_op(:shape, x)
          sy = i_op(:shape, y)
          rx, ry = _broadcast_gradient_args(sx, sy)

          [tf.reshape(tf.reduce_sum(tf.div(grad, y), rx), sx),
           tf.reshape(tf.reduce_sum(grad * tf.div(tf.div(-x, y), y), ry), sy)]
        when :mod
          sx = tf.shape(x)
          sy = tf.shape(y)
          rx, ry = _broadcast_gradient_args(sx, sy)
          floor_xy = tf.floor_div(x, y)
          gx = tf.reshape(tf.reduce_sum(grad, rx), sx)
          gy = tf.reshape(tf.reduce_sum(grad * tf.negative(floor_xy), ry), sy)

          [gx, gy]
        when :squared_difference
          sx = i_op(:shape, x)
          sy = i_op(:shape, y)
          rx, ry = _broadcast_gradient_args(sx, sy)

          x_grad = tf.mul(2.0, grad) * (x - y)

          [tf.reshape(tf.reduce_sum(x_grad, rx), sx),
           tf.reshape(-tf.reduce_sum(x_grad, ry), sy)]
        when :mat_mul
          t_a = node.options[:transpose_a]
          t_b = node.options[:transpose_b]

          if !t_a && !t_b
            grad_a = tf.matmul(grad, y, transpose_b: true)
            grad_b = tf.matmul(x, grad, transpose_a: true)
          elsif !ta && tb
            grad_a = tf.matmul(grad, y)
            grad_b = tf.matmul(grad, x, transpose_a: true)
          elsif t_a && !t_b
            grad_a = tf.matmul(y, grad, transpose_b: true)
            grad_b = tf.matmul(x, grad)
          elsif t_a && t_b
            grad_a = tf.matmul(y, grad, transpose_a: true, transpose_b: true)
            grad_b = tf.matmul(grad, x, transpose_a: true, transpose_b: true)
          end

          [grad_a, grad_b]
        when :sin
          grad * tf.cos(x)
        when :tanh
          grad * i_op(:tanh_grad, x)
        when :pow
          z = node
          sx = tf.shape(x)
          sy = tf.shape(y)
          rx, ry = _broadcast_gradient_args(sx, sy)
          gx = tf.reduce_sum(grad * y * tf.pow(x, y - 1), rx)

          log_x = tf.where(x > 0, tf.log(x), tf.zeros_like(x))
          gy = tf.reduce_sum(grad * z * log_x, ry)

          [gx, gy]
        when :abs
          grad * tf.sign(x)
        when :log
          grad * tf.reciprocal(x)
        when :cos
          -grad * tf.sin(x)
        when :max
          _min_or_max_grad(node.inputs, grad, ->(x, y) { tf.greater_equal(x, y) } )
        when :min
          _min_or_max_grad(node.inputs, grad, ->(x, y) { tf.less_equal(x, y) } )
        when :tan
          secx = tf.reciprocal(tf.cos(x))
          secx2 = tf.square(secx)
          grad * secx2
        when :negate
          -grad
        when :exp
          grad * node
        when :identity, :print
          grad
        when :sign
          tf.zeros(tf.shape(x), dtype: x.data_type)
        when :sum
          _sum_grad(x, y, grad)
        when :reciprocal
          -grad * (tf.constant(1, dtype: x.dtype) / x**2)
        when :sqrt
          tf.constant(1, dtype: x.dtype) / (tf.constant(2, dtype: x.dtype) * tf.sqrt(x)) * grad
        when :stop_gradient
          tf.zeros_like(grad)
        when :square
          y = tf.constant(2.0, dtype: x.dtype)
          tf.multiply(grad, tf.multiply(x, y))
        when :where
          x_mask = i_op(:where, i_op(:ones_like, x), i_op(:zeros_like, y), pred: node.options[:pred])
          y_mask = i_op(:where, i_op(:zeros_like, x), i_op(:ones_like, y), pred: node.options[:pred])
          [x_mask * grad, y_mask * grad]
        when :cond
          x_cond = i_op(:cond, i_op(:ones_like, x), i_op(:zeros_like, y), pred: node.options[:pred])
          y_cond = i_op(:cond, i_op(:zeros_like, x), i_op(:ones_like, x), pred: node.options[:pred])
          [x_cond * grad, y_cond * grad]
        when :mean
          sum_grad = _sum_grad(x, y, grad)[0]
          input_shape = tf.shape(x)
          output_shape = tf.shape(node)
          factor = _safe_shape_div(tf.reduce_prod(input_shape), tf.reduce_prod(output_shape))
          tf.div(sum_grad, tf.cast(factor, sum_grad.data_type))
        when :log1p
          grad * tf.reciprocal(i_cons(1, dtype: grad.data_type) + x)
        when :sigmoid
          i_op(:sigmoid_grad, x, grad)
        when :sigmoid_grad
          gb = grad * y
          [gb - 2.0 * gb * x, i_op(:sigmoid_grad, x, grad)]
        when :softmax
          i_op(:softmax_grad, x, grad)
        when :softmax_cross_entropy_with_logits_v2
          [i_op(:softmax_cross_entropy_with_logits_v2_grad, x, y, grad), nil]
        when :floor, :ceil
          # non differentiable
          nil
        when :zeros_like
          # non differentiable
          nil
        when :argmin, :argmax, :floor_div
          # non differentiable
          [nil, nil]
        else
          raise "no derivative op for #{node.operation}"
        end
      end
    end

    def self._broadcast_gradient_args(input_a, input_b)
      res = _op(:broadcast_gradient_args, input_a, input_b)
      [res[0], res[1]]
    end

    def self._broadcast_transform(input_a, input_b)
      _op(:broadcast_transform, input_a, input_b)
    end

    def self._safe_shape_div(arg_x, arg_y)
      _op(:floor_div, arg_x, tf.maximum(arg_y, 1))
    end

    def self._sum_grad(arg_x, arg_y, grad)
      input_shape = _op(:shape, arg_x)
      output_shape_kept_dims = tf.reduced_shape(input_shape, arg_y)
      tile_scaling = _safe_shape_div(input_shape, output_shape_kept_dims)
      new_grad = _op(:reshape, grad, output_shape_kept_dims)

      grad = _op(:cond, _op(:fill, input_shape, grad), _op(:tile, new_grad, tile_scaling), pred: _op(:rank, grad).zero?)

      [grad, nil]
    end

    def self._op_supports_broadcast?(node)
      return true if %i[add sub div mul pow].include?(node.operation)
      false
    end

    def self._min_or_max_grad(inputs, grad, selector_op)
      x = inputs[0]
      y = inputs[1]
      gdtype = grad.data_type
      sx = tf.shape(x)
      sy = tf.shape(y)
      gradshape = tf.shape(grad)
      zeros = tf.zeros(gradshape, dtype: gdtype)
      xmask = selector_op.call(x, y)
      rx, ry = _broadcast_gradient_args(sx, sy)
      xgrad = tf.where(xmask, grad, zeros, name: 'x')
      ygrad = tf.where(xmask, zeros, grad, name: 'y')
      gx = tf.reshape(tf.reduce_sum(xgrad, rx), sx)
      gy = tf.reshape(tf.reduce_sum(ygrad, ry), sy)
      [gx, gy]
    end

    def self._include?(arr, obj)
      arr.each { |a| return true if a.equal?(obj) }
      false
    end
  end
end